Cited by 1,908Advanced Causal Inference TechniquesStatistical Methods and Bayesian InferencePsychometric Methodologies and Testing

The DOI matches its Crossref record; 57 of the 58 references it cites were found in a registry or an index.

Advantages of Monte Carlo Confidence Intervals for Indirect Effects

Preacher, Kristopher J., Selig, James P.·2012·Communication Methods and Measures

Abstract

Monte Carlo simulation is a useful but underutilized method of constructing confidence intervals for indirect effects in mediation analysis. The Monte Carlo confidence interval method has several distinct advantages over rival methods. Its performance is comparable to other widely accepted methods of interval construction, it can be used when only summary data are available, it can be used in situations where rival methods (e.g., bootstrapping and distribution of the product methods) are difficult or impossible, and it is not as computer-intensive as some other methods. In this study we discuss Monte Carlo confidence intervals for indirect effects, report the results of a simulation study comparing their performance to that of competing methods, demonstrate the method in applied examples, and discuss several software options for implementation in applied settings.

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Research works printed but not linked: 7

  1. Efron B., 1982, Society of Industrial and Applied Mathematics CBMS-NSF Monographs, 38
  2. Hayes A. F., 2011, Indirect and direct effects of a multicategorical causal agent in statistical mediation analysis, (under review)
  3. Lockwood C. M., 1998, Proceedings of the 23rd Annual Meeting of SAS Users Group International, 997
  4. MacKinnon D. P., 2000, Multivariate applications in substance use research: New methods for new questions, 141
  5. Muthén B. O., 2011, Applications of causally defined direct and indirect effects in mediation analysis using SEM in Mplus
  6. Muthén L. K., 1998, Mplus user's guide
  7. Williams, J. 2004. “Resampling and distribution of the product methods for testing indirect effects in complex models”. Unpublished doctoral dissertation). Tempe, Arizona: Arizona State University.

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Metadata

Authors
Preacher, K. J., & Selig, J. P.
Affiliations
  • Vanderbilt University (US)
  • University of New Mexico (US)
Corresponding author
Preacher, Kristopher J.
Venue
Communication Methods and Measures
Published
2012-04
Volume
6
Issue
2
Pages
77-98
ISSN
  • 1931-2458 (ISSN-L)
  • 1931-2466
OpenAlex
W2104411852
Cited by
1,908
References
58
Citations by year
2026: 237 · 2025: 199 · 2024: 195 · 2023: 183 · 2022: 202 · 2021: 184 · 2020: 138 · 2019: 142 · 2018: 115 · 2017: 90 · 2016: 76 · 2015: 67 · 2014: 52 · 2013: 25 · 2012: 3
FWCI
45.24
Type
journal article
Language
en
Open access
closed
Locations
Communication Methods and Measures (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Keywords
indirect effects, mediation analysis, confidence interval construction, bootstrapping, simulation study, summary data, software implementation

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